# Mapping Gamma-Ray Bursts: Distinguishing Progenitor Systems Through Machine Learning

## TL;DR

This paper uses machine learning techniques to analyze gamma-ray burst data, revealing distinct regions associated with different progenitor systems and identifying potential new sub-populations based on their properties.

## Contribution

It introduces a novel application of UMAP embedding to classify GRB progenitors and explore their sub-populations in a unified visual framework.

## Key findings

- Radio-bright and dark GRBs are distributed without clear clustering.
- Low luminosity GRBs are associated with collapsar regions.
- Long-duration GRBs form a distinct cluster, suggesting a unique sub-population.

## Abstract

We present an analysis of gamma-ray burst (GRB) progenitor classification, through their positions on a Uniform Manifold Approximation and Projection (UMAP) plot, constructed by Negro et al. 2024, from Fermi-GBM waterfall plots. The embedding plot has a head-tail morphology, in which GRBs with confirmed progenitors (e.g. collapsars vs. binary neutron star mergers) fall in distinct regions. We investigate the positions of various proposed sub-populations of GRBs, including those with and without radio afterglow emission, those with the lowest intrinsic luminosity, and those with the longest lasting prompt gamma-ray duration. The radio-bright and radio-dark GRBs fall in the head region of the embedding plot with no distinctive clustering, although the sample size is small. Our low luminosity GRBs fall in the head/collapsar region. A continuous duration gradient reveals an interesting cluster of the longest GRBs ($T_{90} > 100s$) in a distinct region of the plot, possibly warranting further investigation.